1
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Jaeger-Honz S, Klein K, Schreiber F. Systematic analysis, aggregation and visualisation of interaction fingerprints for molecular dynamics simulation data. J Cheminform 2024; 16:28. [PMID: 38475907 DOI: 10.1186/s13321-024-00822-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Accepted: 03/02/2024] [Indexed: 03/14/2024] Open
Abstract
Computational methods such as molecular docking or molecular dynamics (MD) simulations have been developed to simulate and explore the interactions between biomolecules. However, the interactions obtained using these methods are difficult to analyse and evaluate. Interaction fingerprints (IFPs) have been proposed to derive interactions from static 3D coordinates and transform them into 1D bit vectors. More recently, the concept has been applied to derive IFPs from MD simulations, which adds a layer of complexity by adding the temporal motion and dynamics of a system. As a result, many IFPs are obtained from one MD simulation, resulting in a large number of individual IFPs that are difficult to analyse compared to IFPs derived from static 3D structures. Scientific contribution: We introduce a new method to systematically aggregate IFPs derived from MD simulation data. In addition, we propose visualisations to effectively analyse and compare IFPs derived from MD simulation data to account for the temporal evolution of interactions and to compare IFPs across different MD simulations. This has been implemented as a freely available Python library and can therefore be easily adopted by other researchers and to different MD simulation datasets.
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Affiliation(s)
- Sabrina Jaeger-Honz
- Department of Computer and Information Science, University of Konstanz, Universitätsstrasse 10, 78464, Constance, Germany.
| | - Karsten Klein
- Department of Computer and Information Science, University of Konstanz, Universitätsstrasse 10, 78464, Constance, Germany
| | - Falk Schreiber
- Department of Computer and Information Science, University of Konstanz, Universitätsstrasse 10, 78464, Constance, Germany
- Faculty of Information Technology, Monash University, Clayton, VIC, 3800, Australia
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2
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Ji KY, Liu C, Liu ZQ, Deng YF, Hou TJ, Cao DS. Comprehensive assessment of nine target prediction web services: which should we choose for target fishing? Brief Bioinform 2023; 24:6995377. [PMID: 36681902 DOI: 10.1093/bib/bbad014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Revised: 12/29/2022] [Accepted: 01/03/2023] [Indexed: 01/23/2023] Open
Abstract
Identification of potential targets for known bioactive compounds and novel synthetic analogs is of considerable significance. In silico target fishing (TF) has become an alternative strategy because of the expensive and laborious wet-lab experiments, explosive growth of bioactivity data and rapid development of high-throughput technologies. However, these TF methods are based on different algorithms, molecular representations and training datasets, which may lead to different results when predicting the same query molecules. This can be confusing for practitioners in practical applications. Therefore, this study systematically evaluated nine popular ligand-based TF methods based on target and ligand-target pair statistical strategies, which will help practitioners make choices among multiple TF methods. The evaluation results showed that SwissTargetPrediction was the best method to produce the most reliable predictions while enriching more targets. High-recall similarity ensemble approach (SEA) was able to find real targets for more compounds compared with other TF methods. Therefore, SwissTargetPrediction and SEA can be considered as primary selection methods in future studies. In addition, the results showed that k = 5 was the optimal number of experimental candidate targets. Finally, a novel ensemble TF method based on consensus voting is proposed to improve the prediction performance. The precision of the ensemble TF method outperforms the individual TF method, indicating that the ensemble TF method can more effectively identify real targets within a given top-k threshold. The results of this study can be used as a reference to guide practitioners in selecting the most effective methods in computational drug discovery.
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Affiliation(s)
- Kai-Yue Ji
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha 410013, Hunan, P. R. China
| | - Chong Liu
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha 410013, Hunan, P. R. China
| | - Zhao-Qian Liu
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha 410013, Hunan, P. R. China
| | - Ya-Feng Deng
- CarbonSilicon AI Technology Co., Ltd, Hangzhou, Zhejiang 310018, China
| | - Ting-Jun Hou
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China
| | - Dong-Sheng Cao
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha 410013, Hunan, P. R. China
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3
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Wang DD, Chan MT, Yan H. Structure-based protein-ligand interaction fingerprints for binding affinity prediction. Comput Struct Biotechnol J 2021; 19:6291-6300. [PMID: 34900139 PMCID: PMC8637032 DOI: 10.1016/j.csbj.2021.11.018] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Revised: 11/09/2021] [Accepted: 11/13/2021] [Indexed: 11/17/2022] Open
Abstract
Binding affinity prediction (BAP) using protein–ligand complex structures is crucial to computer-aided drug design, but remains a challenging problem. To achieve efficient and accurate BAP, machine-learning scoring functions (SFs) based on a wide range of descriptors have been developed. Among those descriptors, protein–ligand interaction fingerprints (IFPs) are competitive due to their simple representations, elaborate profiles of key interactions and easy collaborations with machine-learning algorithms. In this paper, we have adopted a building-block-based taxonomy to review a broad range of IFP models, and compared representative IFP-based SFs in target-specific and generic scoring tasks. Atom-pair-counts-based and substructure-based IFPs show great potential in these tasks.
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Affiliation(s)
- Debby D Wang
- School of Health Science and Engineering, University of Shanghai for Science and Technology, 516 Jungong Rd, Shanghai 200093, China
| | - Moon-Tong Chan
- School of Science and Technology, Hong Kong Metropolitan University, 30 Good Shepherd St, Ho Man Tin, Hong Kong
| | - Hong Yan
- Department of Electrical Engineering, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong
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4
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Shen Z, Yan YH, Yang S, Zhu S, Yuan Y, Qiu Z, Jia H, Wang R, Li GB, Li H. ProfKin: A comprehensive web server for structure-based kinase profiling. Eur J Med Chem 2021; 225:113772. [PMID: 34411891 DOI: 10.1016/j.ejmech.2021.113772] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Revised: 06/30/2021] [Accepted: 08/09/2021] [Indexed: 11/16/2022]
Abstract
Protein kinases are central mediators of signal-transduction cascades and attractive drug targets for therapeutic intervention. Since kinases are structurally and mechanistically related to each other, kinase inhibitor selectivity is often investigated by kinase profiling and considered as an important index for drug discovery. We here describe a versatile web server termed ProfKin for structure-based kinase profiling, which is based on a kinase-ligand focused database (KinLigDB). It provides all ready-to-use 3D structure coordinates of 4219 kinase-ligand complex structures covering 297 human kinases and the associated information, particularly including binding site type, binding ligand type, interaction fingerprints, downstream molecules and related human diseases. The web server works via predicting possible binding modes for the query molecule, prioritizing the binding modes guided by an interaction fingerprint analysis method, and giving a list of ranked kinases by a comprehensive index. Users can freely select entire or part of the KinLigDB database, e.g. via subfamily and binding site type, to customize the profiling contents. The superimpositions of the predicted binding poses of the query molecule with reference binding modes can be visually inspected on the website. The additional classification attributes and phylogenetic tree are also given for each top-ranked kinase.
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Affiliation(s)
- Zihao Shen
- Shanghai Key Laboratory of New Drug Design, East China University of Science and Technology, Shanghai, 200237, China
| | - Yu-Hang Yan
- Key Laboratory of Drug Targeting and Drug Delivery System of Ministry of Education, West China School of Pharmacy, Sichuan University, Sichuan, 610041, China
| | - Shuo Yang
- Shanghai Key Laboratory of New Drug Design, East China University of Science and Technology, Shanghai, 200237, China
| | - Sang Zhu
- Key Laboratory of Drug Targeting and Drug Delivery System of Ministry of Education, West China School of Pharmacy, Sichuan University, Sichuan, 610041, China
| | - Yuan Yuan
- Key Laboratory of Drug Targeting and Drug Delivery System of Ministry of Education, West China School of Pharmacy, Sichuan University, Sichuan, 610041, China
| | - Zhiqiang Qiu
- Key Laboratory of Drug Targeting and Drug Delivery System of Ministry of Education, West China School of Pharmacy, Sichuan University, Sichuan, 610041, China
| | - Huan Jia
- Key Laboratory of Drug Targeting and Drug Delivery System of Ministry of Education, West China School of Pharmacy, Sichuan University, Sichuan, 610041, China
| | - Ruiqiong Wang
- Key Laboratory of Drug Targeting and Drug Delivery System of Ministry of Education, West China School of Pharmacy, Sichuan University, Sichuan, 610041, China
| | - Guo-Bo Li
- Key Laboratory of Drug Targeting and Drug Delivery System of Ministry of Education, West China School of Pharmacy, Sichuan University, Sichuan, 610041, China.
| | - Honglin Li
- Shanghai Key Laboratory of New Drug Design, East China University of Science and Technology, Shanghai, 200237, China.
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Deep learning in target prediction and drug repositioning: Recent advances and challenges. Drug Discov Today 2021; 27:1796-1814. [PMID: 34718208 DOI: 10.1016/j.drudis.2021.10.010] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2021] [Revised: 09/02/2021] [Accepted: 10/21/2021] [Indexed: 12/12/2022]
Abstract
Drug repositioning is an attractive strategy for discovering new therapeutic uses for approved or investigational drugs, with potentially shorter development timelines and lower development costs. Various computational methods have been used in drug repositioning, promoting the efficiency and success rates of this approach. Recently, deep learning (DL) has attracted wide attention for its potential in target prediction and drug repositioning. Here, we provide an overview of the basic principles of commonly used DL architectures and their applications in target prediction and drug repositioning, and discuss possible ways of dealing with current challenges to help achieve its expected potential for drug repositioning.
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6
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Ning XL, Li YZ, Huo C, Deng J, Gao C, Zhu KR, Wang M, Wu YX, Yu JL, Ren YL, Luo ZY, Li G, Chen Y, Wang SY, Peng C, Yang LL, Wang ZY, Wu Y, Qian S, Li GB. X-ray Structure-Guided Discovery of a Potent, Orally Bioavailable, Dual Human Indoleamine/Tryptophan 2,3-Dioxygenase (hIDO/hTDO) Inhibitor That Shows Activity in a Mouse Model of Parkinson's Disease. J Med Chem 2021; 64:8303-8332. [PMID: 34110158 DOI: 10.1021/acs.jmedchem.1c00303] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
Human indoleamine 2,3-dioxygenase 1 (hIDO1) and tryptophan 2,3-dioxygenase (hTDO) have been closely linked to the pathogenesis of Parkinson's disease (PD); nevertheless, development of dual hIDO1 and hTDO inhibitors to evaluate their potential efficacy against PD is still lacking. Here, we report biochemical, biophysical, and computational analyses revealing that 1H-indazole-4-amines inhibit both hIDO1 and hTDO by a mechanism involving direct coordination with the heme ferrous and ferric states. Crystal structure-guided optimization led to 23, which manifested IC50 values of 0.64 and 0.04 μM to hIDO1 and hTDO, respectively, and had good pharmacokinetic properties and brain penetration in mice. 23 showed efficacy against the 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine-induced mouse motor coordination deficits, comparable to Madopar, an anti-PD medicine. Further studies revealed that different from Madopar, 23 likely has specific anti-PD mechanisms involving lowering IDO1 expression, alleviating dopaminergic neurodegeneration, reducing inflammatory cytokines and quinolinic acid in mouse brain, and increasing kynurenic acid in mouse blood.
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MESH Headings
- 1-Methyl-4-phenyl-1,2,3,6-tetrahydropyridine
- Animals
- Brain/pathology
- Cell Line, Tumor
- Crystallography, X-Ray
- Enzyme Inhibitors/chemical synthesis
- Enzyme Inhibitors/metabolism
- Enzyme Inhibitors/therapeutic use
- Humans
- Indazoles/chemical synthesis
- Indazoles/metabolism
- Indazoles/therapeutic use
- Indoleamine-Pyrrole 2,3,-Dioxygenase/antagonists & inhibitors
- Indoleamine-Pyrrole 2,3,-Dioxygenase/metabolism
- Male
- Mice, Inbred C57BL
- Molecular Docking Simulation
- Molecular Structure
- Neuroprotective Agents/chemical synthesis
- Neuroprotective Agents/metabolism
- Neuroprotective Agents/therapeutic use
- Parkinson Disease, Secondary/chemically induced
- Parkinson Disease, Secondary/drug therapy
- Parkinson Disease, Secondary/pathology
- Protein Binding
- Structure-Activity Relationship
- Tryptophan Oxygenase/antagonists & inhibitors
- Tryptophan Oxygenase/metabolism
- Mice
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Affiliation(s)
- Xiang-Li Ning
- Key Laboratory of Drug-Targeting and Drug Delivery System of the Education Ministry and Sichuan Province, Department of Medicinal Chemistry, West China School of Pharmacy, Sichuan University, Chengdu 610041, China
| | - Yu-Zhi Li
- State Key Laboratory of Southwestern Chinese Medicine Resources, School of Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China
| | - Cui Huo
- Department of Pharmaceutical Engineering, College of Food and Bioengineering, Xihua University, Chengdu 610039, China
| | - Ji Deng
- Key Laboratory of Drug-Targeting and Drug Delivery System of the Education Ministry and Sichuan Province, Department of Medicinal Chemistry, West China School of Pharmacy, Sichuan University, Chengdu 610041, China
| | - Cheng Gao
- Department of Pharmaceutical Engineering, College of Food and Bioengineering, Xihua University, Chengdu 610039, China
| | - Kai-Rong Zhu
- Key Laboratory of Drug-Targeting and Drug Delivery System of the Education Ministry and Sichuan Province, Department of Medicinal Chemistry, West China School of Pharmacy, Sichuan University, Chengdu 610041, China
| | - Miao Wang
- State Key Laboratory of Southwestern Chinese Medicine Resources, School of Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China
| | - Yu-Xiang Wu
- Department of Pharmaceutical Engineering, College of Food and Bioengineering, Xihua University, Chengdu 610039, China
| | - Jun-Lin Yu
- Key Laboratory of Drug-Targeting and Drug Delivery System of the Education Ministry and Sichuan Province, Department of Medicinal Chemistry, West China School of Pharmacy, Sichuan University, Chengdu 610041, China
| | - Ya-Li Ren
- State Key Laboratory of Southwestern Chinese Medicine Resources, School of Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China
| | - Zong-Yuan Luo
- Department of Pharmaceutical Engineering, College of Food and Bioengineering, Xihua University, Chengdu 610039, China
| | - Gen Li
- Key Laboratory of Drug-Targeting and Drug Delivery System of the Education Ministry and Sichuan Province, Department of Medicinal Chemistry, West China School of Pharmacy, Sichuan University, Chengdu 610041, China
| | - Yang Chen
- Department of Pharmaceutical Engineering, College of Food and Bioengineering, Xihua University, Chengdu 610039, China
| | - Si-Yao Wang
- Key Laboratory of Drug-Targeting and Drug Delivery System of the Education Ministry and Sichuan Province, Department of Medicinal Chemistry, West China School of Pharmacy, Sichuan University, Chengdu 610041, China
| | - Cheng Peng
- State Key Laboratory of Southwestern Chinese Medicine Resources, School of Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China
| | - Ling-Ling Yang
- Department of Pharmaceutical Engineering, College of Food and Bioengineering, Xihua University, Chengdu 610039, China
| | - Zhou-Yu Wang
- Department of Pharmaceutical Engineering, College of Food and Bioengineering, Xihua University, Chengdu 610039, China
| | - Yong Wu
- Key Laboratory of Drug-Targeting and Drug Delivery System of the Education Ministry and Sichuan Province, Department of Medicinal Chemistry, West China School of Pharmacy, Sichuan University, Chengdu 610041, China
| | - Shan Qian
- Department of Pharmaceutical Engineering, College of Food and Bioengineering, Xihua University, Chengdu 610039, China
| | - Guo-Bo Li
- Key Laboratory of Drug-Targeting and Drug Delivery System of the Education Ministry and Sichuan Province, Department of Medicinal Chemistry, West China School of Pharmacy, Sichuan University, Chengdu 610041, China
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7
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Qin T, Zhu Z, Wang XS, Xia J, Wu S. Computational representations of protein-ligand interfaces for structure-based virtual screening. Expert Opin Drug Discov 2021; 16:1175-1192. [PMID: 34011222 DOI: 10.1080/17460441.2021.1929921] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
Introduction: Structure-based virtual screening (SBVS) is an essential strategy for hit identification. SBVS primarily uses molecular docking, which exploits the protein-ligand binding mode and associated affinity score for compound ranking. Previous studies have shown that computational representation of protein-ligand interfaces and the later establishment of machine learning models are efficacious in improving the accuracy of SBVS.Areas covered: The authors review the computational methods for representing protein-ligand interfaces, which include the traditional ones that use deliberately designed fingerprints and descriptors and the more recent methods that automatically extract features with deep learning. The effects of these methods on the performance of machine learning models are briefly discussed. Additionally, case studies that applied various computational representations to machine learning are cited with remarks.Expert opinion: It has become a trend to extract binding features automatically by deep learning, which uses a completely end-to-end representation. However, there is still plenty of scope for improvement . The interpretability of deep-learning models, the organization of data management, the quantity and quality of available data, and the optimization of hyperparameters could impact the accuracy of feature extraction. In addition, other important structural factors such as water molecules and protein flexibility should be considered.
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Affiliation(s)
- Tong Qin
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Department of New Drug Research and Development, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Zihao Zhu
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Department of New Drug Research and Development, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xiang Simon Wang
- Artificial Intelligence and Drug Discovery Core Laboratory for District of Columbia Center for AIDS Research (DC CFAR), Department of Pharmaceutical Sciences, College of Pharmacy, Howard University, U.S.A
| | - Jie Xia
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Department of New Drug Research and Development, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Song Wu
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Department of New Drug Research and Development, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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8
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Pasquer QTL, Tsakoumagkos IA, Hoogendoorn S. From Phenotypic Hit to Chemical Probe: Chemical Biology Approaches to Elucidate Small Molecule Action in Complex Biological Systems. Molecules 2020; 25:E5702. [PMID: 33287212 PMCID: PMC7730769 DOI: 10.3390/molecules25235702] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2020] [Revised: 11/27/2020] [Accepted: 12/01/2020] [Indexed: 01/22/2023] Open
Abstract
Biologically active small molecules have a central role in drug development, and as chemical probes and tool compounds to perturb and elucidate biological processes. Small molecules can be rationally designed for a given target, or a library of molecules can be screened against a target or phenotype of interest. Especially in the case of phenotypic screening approaches, a major challenge is to translate the compound-induced phenotype into a well-defined cellular target and mode of action of the hit compound. There is no "one size fits all" approach, and recent years have seen an increase in available target deconvolution strategies, rooted in organic chemistry, proteomics, and genetics. This review provides an overview of advances in target identification and mechanism of action studies, describes the strengths and weaknesses of the different approaches, and illustrates the need for chemical biologists to integrate and expand the existing tools to increase the probability of evolving screen hits to robust chemical probes.
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Affiliation(s)
| | | | - Sascha Hoogendoorn
- Department of Organic Chemistry, University of Geneva, Quai Ernest-Ansermet 30, 1211 Genève, Switzerland; (Q.T.L.P.); (I.A.T.)
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9
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Istyastono EP, Radifar M, Yuniarti N, Prasasty VD, Mungkasi S. PyPLIF HIPPOS: A Molecular Interaction Fingerprinting Tool for Docking Results of AutoDock Vina and PLANTS. J Chem Inf Model 2020; 60:3697-3702. [PMID: 32687350 DOI: 10.1021/acs.jcim.0c00305] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
We describe here our tool named PyPLIF HIPPOS, which was newly developed to analyze the docking results of AutoDock Vina and PLANTS. Its predecessor, PyPLIF (https://github.com/radifar/pyplif), is a molecular interaction fingerprinting tool for the docking results of PLANTS, exclusively. Unlike its predecessor, PyPLIF HIPPOS speeds up the computational times by separating the reference generation and docking analysis. PyPLIF HIPPOS also offers more options compared to PyPLIF. PyPLIF HIPPOS for Linux is stored as the Supporting Information in this application note and can be accessed in GitHub (https://github.com/radifar/PyPLIF-HIPPOS). Additionally, we present here the application of the tool in a retrospective structure-based virtual screening campaign targeting neuraminidase.
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Affiliation(s)
- Enade P Istyastono
- Faculty of Pharmacy, Sanata Dharma University, Campus 3, Paingan, Maguwoharjo, Depok, Sleman, Yogyakarta 55282, Indonesia.,Center for Molecular Modeling (molmod.org), Sinduadi, Mlati, Sleman, Yogyakarta 55284, Indonesia
| | - Muhammad Radifar
- Center for Molecular Modeling (molmod.org), Sinduadi, Mlati, Sleman, Yogyakarta 55284, Indonesia
| | - Nunung Yuniarti
- Department of Pharmacology and Clinical Pharmacy, Faculty of Pharmacy, Universitas Gadjah Mada, Depok, Sleman, Yogyakarta 55281, Indonesia
| | - Vivitri D Prasasty
- Faculty of Biotechnology, Atma Jaya Catholic University of Indonesia, Jakarta 12930, Indonesia
| | - Sudi Mungkasi
- Department of Mathematics, Faculty of Science and Technology, Sanata Dharma University, Paingan, Maguwoharjo, Depok, Sleman, Yogyakarta 55282, Indonesia
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10
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Du J, Guo J, Kang D, Li Z, Wang G, Wu J, Zhang Z, Fang H, Hou X, Huang Z, Li G, Lu X, Liu X, Ouyang L, Rao L, Zhan P, Zhang X, Zhang Y. New techniques and strategies in drug discovery. CHINESE CHEM LETT 2020. [DOI: 10.1016/j.cclet.2020.03.028] [Citation(s) in RCA: 59] [Impact Index Per Article: 14.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
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11
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Ansar S, Vetrivel U. KinomeRun: An interactive utility for kinome target screening and interaction fingerprint analysis towards holistic visualization on kinome tree. Chem Biol Drug Des 2020; 96:1162-1175. [PMID: 32418310 DOI: 10.1111/cbdd.13705] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2020] [Revised: 04/15/2020] [Accepted: 05/06/2020] [Indexed: 12/27/2022]
Abstract
Kinases are key targets for many of the pathological conditions. Inverse screening of ligands serves as an essential mode to identify potential kinase targets in modern drug discovery research. Hence, we intend to develop KinomeRun, a robust pipeline for inverse screening and kinome tree visualization through the seamless integration of kinome structures, docking and kinome-drug interaction fingerprint analysis. In this pipeline, the hurdle of residue numbering in kinome is also resolved by creating a common index file with the conserved kinase pocket residues for comparative interaction analysis. KinomeRun can be used to screen the ligands of interest docked against multiple kinase structures in parallel around the kinase binding site and also to filter out the targets with unique interaction patterns. This automation is essential for prioritization of kinase targets that show specificity for a given drug and will also serve as a crucial tool kit for holistic approaches in kinase drug discovery. KinomeRun is developed using python and bash programming language and is distributed freely under the GNU GPL licence-3.0 and can be downloaded at https://github.com/inpacdb/KinomeRun. The tutorial videos for installation, target screening and customized filtration are available at https://www.youtube.com/playlist?list=PLuIaEFtMVgQ7v__WigQH9ilGVxrfI1LKs and also be downloaded for offline viewing from the github link.
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Affiliation(s)
- Samdani Ansar
- Centre for Bioinformatics, Kamalnayan Bajaj Institute for Research in Vision and Ophthalmology, Vision Research Foundation, Sankara Nethralaya, Chennai, India.,School of Chemical and Biotechnology, SASTRA Deemed University, Thanjavur, India
| | - Umashankar Vetrivel
- Centre for Bioinformatics, Kamalnayan Bajaj Institute for Research in Vision and Ophthalmology, Vision Research Foundation, Sankara Nethralaya, Chennai, India.,National Institute of Traditional Medicine, Indian Council of Medical Research, Department of Health Research (Govt. of India), Belagavi, India
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12
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Yan Y, Li G, Li G. Principles and current strategies targeting metallo‐β‐lactamase mediated antibacterial resistance. Med Res Rev 2020; 40:1558-1592. [PMID: 32100311 DOI: 10.1002/med.21665] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2019] [Revised: 12/18/2019] [Accepted: 02/11/2020] [Indexed: 12/24/2022]
Affiliation(s)
- Yu‐Hang Yan
- Key Laboratory of Drug‐Targeting and Drug Delivery System of the Education Ministry and Sichuan Province, Sichuan Engineering Laboratory for Plant‐Sourced Drug and Sichuan Research Center for Drug Precision Industrial Technology, West China School of PharmacySichuan UniversityChengdu Sichuan China
| | - Gen Li
- Key Laboratory of Drug‐Targeting and Drug Delivery System of the Education Ministry and Sichuan Province, Sichuan Engineering Laboratory for Plant‐Sourced Drug and Sichuan Research Center for Drug Precision Industrial Technology, West China School of PharmacySichuan UniversityChengdu Sichuan China
| | - Guo‐Bo Li
- Key Laboratory of Drug‐Targeting and Drug Delivery System of the Education Ministry and Sichuan Province, Sichuan Engineering Laboratory for Plant‐Sourced Drug and Sichuan Research Center for Drug Precision Industrial Technology, West China School of PharmacySichuan UniversityChengdu Sichuan China
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13
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Pal A, Tripathi A. Quercetin inhibits carbapenemase and efflux pump activities among carbapenem‐resistant Gram‐negative bacteria. APMIS 2020; 128:251-259. [DOI: 10.1111/apm.13015] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2019] [Accepted: 11/18/2019] [Indexed: 12/01/2022]
Affiliation(s)
- Arijit Pal
- Department of Biochemistry and Medical Biotechnology Calcutta School of Tropical Medicine KolkataIndia
| | - Anusri Tripathi
- Department of Biochemistry and Medical Biotechnology Calcutta School of Tropical Medicine Kolkata India
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14
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Wang YL, Liu S, Yu ZJ, Lei Y, Huang MY, Yan YH, Ma Q, Zheng Y, Deng H, Sun Y, Wu C, Yu Y, Chen Q, Wang Z, Wu Y, Li GB. Structure-Based Development of (1-(3′-Mercaptopropanamido)methyl)boronic Acid Derived Broad-Spectrum, Dual-Action Inhibitors of Metallo- and Serine-β-lactamases. J Med Chem 2019; 62:7160-7184. [PMID: 31269398 DOI: 10.1021/acs.jmedchem.9b00735] [Citation(s) in RCA: 39] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Affiliation(s)
- Yao-Ling Wang
- Key Laboratory of Drug-Targeting and Drug Delivery System of the Education Ministry, Sichuan Engineering Laboratory for Plant-Sourced Drug and Sichuan Research Center for Drug Precision Industrial Technology, West China School of Pharmacy, Sichuan University, Sichuan 610041, China
| | - Sha Liu
- Key Laboratory of Drug-Targeting and Drug Delivery System of the Education Ministry, Sichuan Engineering Laboratory for Plant-Sourced Drug and Sichuan Research Center for Drug Precision Industrial Technology, West China School of Pharmacy, Sichuan University, Sichuan 610041, China
| | - Zhu-Jun Yu
- Key Laboratory of Drug-Targeting and Drug Delivery System of the Education Ministry, Sichuan Engineering Laboratory for Plant-Sourced Drug and Sichuan Research Center for Drug Precision Industrial Technology, West China School of Pharmacy, Sichuan University, Sichuan 610041, China
| | - Yuan Lei
- Key Laboratory of Drug-Targeting and Drug Delivery System of the Education Ministry, Sichuan Engineering Laboratory for Plant-Sourced Drug and Sichuan Research Center for Drug Precision Industrial Technology, West China School of Pharmacy, Sichuan University, Sichuan 610041, China
| | - Meng-Yi Huang
- Key Laboratory of Drug-Targeting and Drug Delivery System of the Education Ministry, Sichuan Engineering Laboratory for Plant-Sourced Drug and Sichuan Research Center for Drug Precision Industrial Technology, West China School of Pharmacy, Sichuan University, Sichuan 610041, China
| | - Yu-Hang Yan
- Key Laboratory of Drug-Targeting and Drug Delivery System of the Education Ministry, Sichuan Engineering Laboratory for Plant-Sourced Drug and Sichuan Research Center for Drug Precision Industrial Technology, West China School of Pharmacy, Sichuan University, Sichuan 610041, China
| | - Qiang Ma
- Key Laboratory of Drug-Targeting and Drug Delivery System of the Education Ministry, Sichuan Engineering Laboratory for Plant-Sourced Drug and Sichuan Research Center for Drug Precision Industrial Technology, West China School of Pharmacy, Sichuan University, Sichuan 610041, China
| | - Yang Zheng
- Key Laboratory of Drug-Targeting and Drug Delivery System of the Education Ministry, Sichuan Engineering Laboratory for Plant-Sourced Drug and Sichuan Research Center for Drug Precision Industrial Technology, West China School of Pharmacy, Sichuan University, Sichuan 610041, China
| | - Hui Deng
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, West China Medical School, Sichuan University, Chengdu 610041, China
| | - Ying Sun
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, West China Medical School, Sichuan University, Chengdu 610041, China
| | - Chengyong Wu
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, West China Medical School, Sichuan University, Chengdu 610041, China
| | - Yamei Yu
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, West China Medical School, Sichuan University, Chengdu 610041, China
| | - Qiang Chen
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, West China Medical School, Sichuan University, Chengdu 610041, China
| | - Zhenling Wang
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, West China Medical School, Sichuan University, Chengdu 610041, China
| | - Yong Wu
- Key Laboratory of Drug-Targeting and Drug Delivery System of the Education Ministry, Sichuan Engineering Laboratory for Plant-Sourced Drug and Sichuan Research Center for Drug Precision Industrial Technology, West China School of Pharmacy, Sichuan University, Sichuan 610041, China
| | - Guo-Bo Li
- Key Laboratory of Drug-Targeting and Drug Delivery System of the Education Ministry, Sichuan Engineering Laboratory for Plant-Sourced Drug and Sichuan Research Center for Drug Precision Industrial Technology, West China School of Pharmacy, Sichuan University, Sichuan 610041, China
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15
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Sydow D, Burggraaff L, Szengel A, van Vlijmen HWT, IJzerman AP, van Westen GJP, Volkamer A. Advances and Challenges in Computational Target Prediction. J Chem Inf Model 2019; 59:1728-1742. [DOI: 10.1021/acs.jcim.8b00832] [Citation(s) in RCA: 58] [Impact Index Per Article: 11.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Affiliation(s)
- Dominique Sydow
- In silico Toxicology, Institute of Physiology, Charité − Universitätsmedizin Berlin, Charitéplatz 1, 10117 Berlin, Germany
| | - Lindsey Burggraaff
- Division of Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Leiden University, P.O. Box 9502, 2300 RA, Leiden, The Netherlands
| | - Angelika Szengel
- In silico Toxicology, Institute of Physiology, Charité − Universitätsmedizin Berlin, Charitéplatz 1, 10117 Berlin, Germany
| | - Herman W. T. van Vlijmen
- Division of Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Leiden University, P.O. Box 9502, 2300 RA, Leiden, The Netherlands
- Computational Chemistry, Janssen Research & Development, Turnhoutseweg 30, B-2340 Beerse, Belgium
| | - Adriaan P. IJzerman
- Division of Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Leiden University, P.O. Box 9502, 2300 RA, Leiden, The Netherlands
| | - Gerard J. P. van Westen
- Division of Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Leiden University, P.O. Box 9502, 2300 RA, Leiden, The Netherlands
| | - Andrea Volkamer
- In silico Toxicology, Institute of Physiology, Charité − Universitätsmedizin Berlin, Charitéplatz 1, 10117 Berlin, Germany
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16
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Nogueira MS, Koch O. The Development of Target-Specific Machine Learning Models as Scoring Functions for Docking-Based Target Prediction. J Chem Inf Model 2019; 59:1238-1252. [DOI: 10.1021/acs.jcim.8b00773] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Affiliation(s)
- Mauro S. Nogueira
- Faculty of Chemistry and Chemical Biology, TU Dortmund University, Otto-Hahn-Straße 6, 44227, Dortmund, Germany
| | - Oliver Koch
- Faculty of Chemistry and Chemical Biology, TU Dortmund University, Otto-Hahn-Straße 6, 44227, Dortmund, Germany
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17
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Abstract
Pharmacological science is trying to establish the link between chemicals, targets, and disease-related phenotypes. A plethora of chemical proteomics and structural data have been generated, thanks to the target-based approach that has dominated drug discovery at the turn of the century. There is an invaluable source of information for in silico target profiling. Prediction is based on the principle of chemical similarity (similar drugs bind similar targets) or on first principles from the biophysics of molecular interactions. In the first case, compound comparison is made through ligand-based chemical similarity search or through classifier-based machine learning approach. The 3D techniques are based on 3D structural descriptors or energy-based scoring scheme to infer a binding affinity of a compound with its putative target. More recently, a new approach based on compound set metric has been proposed in which a query compound is compared with a whole of compounds associated with a target or a family of targets. This chapter reviews the different techniques of in silico target profiling and their main applications such as inference of unwanted targets, drug repurposing, or compound prioritization after phenotypic-based screening campaigns.
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18
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Zhou X, Wu M, Xie Y, Li GB, Li T, Xie R, Wang K, Zhang Y, Zou C, Wu W, Wang Q, Wang X, Zhang X, Li J, Li J, Wei YQ. Identification of Glycine Receptor α3 as a Colchicine-Binding Protein. Front Pharmacol 2018; 9:1238. [PMID: 30467477 PMCID: PMC6236057 DOI: 10.3389/fphar.2018.01238] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2018] [Accepted: 10/11/2018] [Indexed: 02/05/2023] Open
Abstract
Colchicine (Col) is considered a kind of highly effective alkaloid for preventing and treating acute gout attacks (flares). However, little is known about the underlying mechanism of Col in pain treatment. We have previously developed a customized virtual target identification method, termed IFPTarget, for small-molecule target identification. In this study, by using IFPTarget and ligand similarity ensemble approach (SEA), we show that the glycine receptor alpha 3 (GlyRα3), which play a key role in the processing of inflammatory pain, is a potential target of Col. Moreover, Col binds directly to the GlyRα3 as determined by the immunoprecipitation and bio-layer interferometry assays using the synthesized Col-biotin conjugate (linked Col and biotin with polyethylene glycol). These results suggest that GlyRα3 may mediate Col-induced suppression of inflammatory pain. However, whether GlyRα3 is the functional target of Col and serves as potential therapeutic target in gouty arthritis requires further investigations.
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Affiliation(s)
- Xikun Zhou
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University and Collaborative Innovation Center of Biotherapy, Chengdu, China
| | - Mingbo Wu
- School of Bioscience and Biotechnology, Chengdu Medical College, Chengdu, China
| | - Yongmei Xie
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University and Collaborative Innovation Center of Biotherapy, Chengdu, China
| | - Guo-Bo Li
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University and Collaborative Innovation Center of Biotherapy, Chengdu, China
| | - Tao Li
- Key Laboratory of Obstetrics and Gynecologic and Pediatric Diseases and Birth Defects of Ministry of Education, Department of Gynecology and Obstetrics, West China Second University Hospital, Sichuan University, Chengdu, China
| | - Rou Xie
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University and Collaborative Innovation Center of Biotherapy, Chengdu, China
| | - Kailun Wang
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University and Collaborative Innovation Center of Biotherapy, Chengdu, China
| | - Yige Zhang
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University and Collaborative Innovation Center of Biotherapy, Chengdu, China
| | - Chaoyu Zou
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University and Collaborative Innovation Center of Biotherapy, Chengdu, China
| | - Wenling Wu
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University and Collaborative Innovation Center of Biotherapy, Chengdu, China
| | - Qi Wang
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University and Collaborative Innovation Center of Biotherapy, Chengdu, China
| | - Xiangwei Wang
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University and Collaborative Innovation Center of Biotherapy, Chengdu, China
| | - Ximu Zhang
- State Key Laboratory of Oral Diseases, National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, China
| | - Jiong Li
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University and Collaborative Innovation Center of Biotherapy, Chengdu, China
| | - Jing Li
- State Key Laboratory of Oral Diseases, National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, China
| | - Yu-Quan Wei
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University and Collaborative Innovation Center of Biotherapy, Chengdu, China
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19
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Yu ZJ, Liu S, Zhou S, Li H, Yang F, Yang LL, Wu Y, Guo L, Li GB. Virtual target screening reveals rosmarinic acid and salvianolic acid A inhibiting metallo- and serine-β-lactamases. Bioorg Med Chem Lett 2018; 28:1037-1042. [PMID: 29477271 DOI: 10.1016/j.bmcl.2018.02.025] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2017] [Revised: 01/31/2018] [Accepted: 02/13/2018] [Indexed: 01/08/2023]
Abstract
Rosmarinic acid (RA), a polyphenolic phytochemical, has broad-spectrum biological and pharmacological activity. A virtual target screening method termed IFPTarget combined with enzyme inhibition assays led to the identification of the clinically relevant metallo-β-lactamase (MBL) VIM-2 as one of unexploited targets of RA. The enzyme kinetic studies indicated that RA is a fully reversible, substrate-competitive VIM-2 inhibitor. The isothermal titration calorimetry (ITC) analyses revealed that the initial binding of RA to VIM-2 is mainly due to enthalpy contribution. Further inhibition assays with RA related compounds revealed that salvianolic acid A, a derivative of RA, manifests potent inhibition to VIM-2, more interestingly, which shows inhibitory activity against the NDM-1, another clinically relevant MBL subtype, and the serine-β-lactamase TEM-1 that is structurally and mechanistically distinct from the VIM-2 and NDM-1.
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Affiliation(s)
- Zhu-Jun Yu
- Key Laboratory of Drug Targeting and Drug Delivery System of Ministry of Education, West China School of Pharmacy, Sichuan University, Sichuan 610041, China
| | - Sha Liu
- Key Laboratory of Drug Targeting and Drug Delivery System of Ministry of Education, West China School of Pharmacy, Sichuan University, Sichuan 610041, China
| | - Shu Zhou
- Key Laboratory of Drug Targeting and Drug Delivery System of Ministry of Education, West China School of Pharmacy, Sichuan University, Sichuan 610041, China
| | - Hui Li
- Key Laboratory of Drug Targeting and Drug Delivery System of Ministry of Education, West China School of Pharmacy, Sichuan University, Sichuan 610041, China
| | - Fan Yang
- College of Food and Bioengineering, Xihua University, Sichuan 610039, China
| | - Ling-Ling Yang
- College of Food and Bioengineering, Xihua University, Sichuan 610039, China
| | - Yong Wu
- Key Laboratory of Drug Targeting and Drug Delivery System of Ministry of Education, West China School of Pharmacy, Sichuan University, Sichuan 610041, China
| | - Li Guo
- Key Laboratory of Drug Targeting and Drug Delivery System of Ministry of Education, West China School of Pharmacy, Sichuan University, Sichuan 610041, China
| | - Guo-Bo Li
- Key Laboratory of Drug Targeting and Drug Delivery System of Ministry of Education, West China School of Pharmacy, Sichuan University, Sichuan 610041, China.
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20
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Yang Z, Zhang Y, Chen X, Li W, Li GB, Wu Y. Total Synthesis and Evaluation of B-Homo Palmatine and Berberine Derivatives as p300 Histone Acetyltransferase Inhibitors. European J Org Chem 2018. [DOI: 10.1002/ejoc.201701693] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Affiliation(s)
- Zhongzhen Yang
- Key Laboratory of Drug Targeting and Drug Delivery System of Ministry of Education; West China School of Pharmacy; Sichuan University; 610041 Sichuan China
| | - Yong Zhang
- Key Laboratory of Drug Targeting and Drug Delivery System of Ministry of Education; West China School of Pharmacy; Sichuan University; 610041 Sichuan China
| | - Xin Chen
- Laboratory of Biotherapy and Cancer Center; West China Hospital; Sichuan University; 610041 West China Medical School China
| | - Weijian Li
- Key Laboratory of Drug Targeting and Drug Delivery System of Ministry of Education; West China School of Pharmacy; Sichuan University; 610041 Sichuan China
| | - Guo-Bo Li
- Key Laboratory of Drug Targeting and Drug Delivery System of Ministry of Education; West China School of Pharmacy; Sichuan University; 610041 Sichuan China
| | - Yong Wu
- Key Laboratory of Drug Targeting and Drug Delivery System of Ministry of Education; West China School of Pharmacy; Sichuan University; 610041 Sichuan China
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21
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#2714, a novel active inhibitor with potent G2/M phase arrest and antitumor efficacy in preclinical models. Cell Death Discov 2018. [PMID: 29531821 PMCID: PMC5841443 DOI: 10.1038/s41420-018-0032-y] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023] Open
Abstract
Arresting cell cycle has been one of the most common approaches worldwide in cancer therapy. Specifically, arresting cells in the G2/M phase is a promising therapeutic approach in the battle against lung cancer. In the present study, we demonstrated the anticancer activities and possible mechanism of compound #2714, which can prompt G2/M phase arrest followed by cell apoptosis induction in Lewis lung carcinoma LL/2 cells. In vitro, #2714 significantly inhibited LL/2 cell viability in a concentration- and time-dependent manner while exhibiting few toxicities on non-cancer cells. The mechanism study showed that cell proliferation inhibition due to the treatment with #2714 correlated with G2/M phase arrest and was followed by LL/2 cell apoptosis. The characterized changes were associated with the downregulation of phosphorylated cell division cycle 25C (Cdc25C) and upregulation of p53. Apoptosis-associated activation of cleaved caspase-3 was also detected. Moreover, #2714 strongly attenuated LL/2 cell proliferation by disrupting the phosphorylation of p44/42 mitogen-activated protein kinase (MAPK). In vivo, intraperitoneal administration of #2714 (25–100 mg/kg/day) to mice bearing established tumors in xenograft models significantly prevented LL/2 tumor growth (58.1%) without detectable toxicity. Compound #2714 significantly increased apoptosis in LL/2 lung cancer cells in mice models, as observed via terminal deoxynucleotidyl transferase (TdT) dUTP nick-end labeling (TUNEL) assay, and the data from an immunohistochemical analysis showed that #2714 remarkably inhibited the proliferation and angiogenesis of lung cancer in vivo. Taken together, our data suggest that #2714 has a high potential anti-lung cancer efficacy with a pathway-specific mechanism of G2/M phase arrest and subsequent apoptosis induction both in vitro and in vivo; its potential to be an anticancer candidate warrants further investigation.
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22
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Liu S, Jing L, Yu ZJ, Wu C, Zheng Y, Zhang E, Chen Q, Yu Y, Guo L, Wu Y, Li GB. (( S )-3-Mercapto-2-methylpropanamido)acetic acid derivatives as metallo-β-lactamase inhibitors: Synthesis, kinetic and crystallographic studies. Eur J Med Chem 2018; 145:649-660. [DOI: 10.1016/j.ejmech.2018.01.032] [Citation(s) in RCA: 33] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2017] [Revised: 01/09/2018] [Accepted: 01/10/2018] [Indexed: 10/18/2022]
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23
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Zhang C, Pu YC, Yu ZJ, Wu CY, Brem J, McDonough MA, Schofield CJ, Li GB, Wu Y. Rh(iii)-Catalyzed directed C–H carbenoid coupling reveals aromatic bisphosphonates inhibiting metallo- and Serine-β-lactamases. Org Chem Front 2018. [DOI: 10.1039/c8qo00009c] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
The C–H coupling of arenes with diazo-methylene-diphosphonates enables efficient synthesis of new diphosphonates, which exhibit dual MBL and SBL inhibition.
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Affiliation(s)
- Chen Zhang
- Key Laboratory of Drug Targeting and Drug Delivery System of Ministry of Education
- West China School of Pharmacy
- Sichuan University Sichuan 610041
- China
| | - Yan-chi Pu
- Key Laboratory of Drug Targeting and Drug Delivery System of Ministry of Education
- West China School of Pharmacy
- Sichuan University Sichuan 610041
- China
| | - Zhu-Jun Yu
- Key Laboratory of Drug Targeting and Drug Delivery System of Ministry of Education
- West China School of Pharmacy
- Sichuan University Sichuan 610041
- China
| | - Cheng-yong Wu
- Key Laboratory of Drug Targeting and Drug Delivery System of Ministry of Education
- West China School of Pharmacy
- Sichuan University Sichuan 610041
- China
| | - Jürgen Brem
- Department of Chemistry
- University of Oxford
- Oxford
- UK
| | | | | | - Guo-Bo Li
- Key Laboratory of Drug Targeting and Drug Delivery System of Ministry of Education
- West China School of Pharmacy
- Sichuan University Sichuan 610041
- China
| | - Yong Wu
- Key Laboratory of Drug Targeting and Drug Delivery System of Ministry of Education
- West China School of Pharmacy
- Sichuan University Sichuan 610041
- China
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